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All Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI IJENSET
Khairul Umam
Universitas Muhammadiyah Lamongan
Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Public Health

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Stroke Risk Prediction Using CatBoost with an Explainable Artificial Intelligence Approach Khairul Umam; M Wicaksana Wibowo Sadewa
Indonesian Journal of Engineering, Science and Technology Vol. 3 No. 1 (2026): VOL. 03 NO. 01 (JUNE 2026)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v3i1.1490

Abstract

Stroke is among the main causes of death worldwide. According to the World Health Organization (WHO), strokes, including ischemic and hemorrhagic, account for around 11% of global mortality. Therefore, early prediction is crucial as part of efforts to prevent the risk of stroke and to assist healthcare professionals in clinical decision-making. This work aims to develop a stroke risk prediction model using the CatBoost algorithm, and to interpret the prediction results using an Explainable Artificial Intelligence (XAI) approach through the SHAP method. The CatBoost model's evaluation results demonstrate strong performance, with AUC = 0.98, an F1-score = 0.91, precision = 0.92, recall = 0.90, and accuracy of 0.93. Furthermore, the XAI analysis utilizing SHAP showed that the CatBoost model not only delivers highly accurate predictions but also successfully identifies the most relevant features leading to stroke risk, namely age, body mass index (BMI), and mean level of glucose. Finally, a comparative examination with various different machine learning models demonstrates that the CatBoost model obtains the best performance and is extremely useful in predicting stroke risk.
Analysis of the Document Management Information System Using the Rapid Application Development Method M. Nurul ihsan; Khairul Umam; Mala Rosa Aprillya; Darmawan
Indonesian Journal of Engineering, Science and Technology Vol. 3 No. 1 (2026): VOL. 03 NO. 01 (JUNE 2026)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v3i1.1512

Abstract

The rapid advancement of information technology has pushed higher education institutions to improve document management efficiency, yet many study programs still rely on manual processes or non-integrated storage, causing data duplication, retrieval difficulties, weak security, and inefficient archiving. This study aims to develop a web-based Study Program Document Management Information System using the Rapid Application Development (RAD) method to enable centralized, integrated, and secure document management. The RAD approach comprised four stages: requirements planning, user design, system construction, and testing and implementation. Data were collected through field observations, stakeholder interviews, and analyses of hardware and software requirements. The system was built using PHP and MariaDB as the RDBMS, providing key features such as login authentication with CAPTCHA security, document upload and download, categorization, search, and user management. Based on Black Box Testing, all 35 test cases were executed successfully without failures, yielding a system validity of 100%. Therefore, the developed system is considered valid, feasible, and effective in improving digital document management, administrative efficiency, and accreditation support for study programs. Keywords: Document Management System; Rapid Application Development; Information System; Black Box Testing; Study Program.
An EfficientNetV2-Based for Alzheimer’s Disease Classification M Sadewa Wicaksana Wibowo; Khairul Umam
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5311

Abstract

In Indonesia, Alzheimer’s disease has emerged as a critical public health priority. This neurodegenerative disorder is characterized by the gradual erosion of memory, linguistic capabilities, and problem-solving skills resulting from irreversible neuronal damage. Magnetic Resonance Imaging (MRI) is commonly used for early diagnosis; however, manual interpretation of MRI scans is time-consuming and subject to inter-observer variability among medical professionals. Recent advances in artificial intelligence have enabled automated analysis of MRI images for Alzheimer’s disease detection, yet many existing approaches rely on deep learning architectures with high computational complexity. To address this limitation, this study proposes a lightweight deep convolutional network based on EfficientNetV2 for Alzheimer’s disease classification using brain MRI images. Data augmentation techniques, including random rotation, affine transformation, horizontal and vertical flipping and normalization are applied to enhance model generalization. Two EfficientNetV2 variants, EfficientNetV2_s and EfficientNetV2_m, are evaluated and compared using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate that EfficientNetV2_s achieves superior performance, attaining an accuracy, precision, recall, and F1-score of approximately 0.90, while EfficientNetV2_m achieves corresponding values of approximately 0.81, indicating lower generalization capability. These results confirm that the smaller EfficientNetV2_s model provides more accurate and reliable classification performance despite its reduced computational complexity.Keywords - Alzheimer’s Disease, Classification, Convolutional Neural Networks, Deep Learning.
Co-Authors Darmawan M Sadewa Wicaksana Wibowo M Wicaksana Wibowo Sadewa M. Nurul ihsan Mala Rosa Aprillya